ROBOT_NUM
The purpose is to evaluate the relationship between patients, and compress their information to the 2-dimensional. While we have some outlier in the data, because there is a patient who take 367 days to discharge after the surgery. We then consider delete it.
In fact, t-SNE is a visualization method, it utilizes simulating low-dimensional data points by t-distributed high-dimensional points.
Between “robot” and “senhance-robot”, it seems a little difference between them. Currently, the plots could not display which variables are the main effects.
summary(lm_robot)
##
## Call:
## lm(formula = Post_LOS ~ ., data = ROBOT_NUMlm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.1783 -0.4266 -0.2658 0.0179 7.4986
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.90688 0.61283 4.743 2.93e-06 ***
## AGE_group 0.08888 0.08034 1.106 0.26926
## Sex 0.69262 0.22333 3.101 0.00206 **
## BMI_group2 0.05704 0.10855 0.525 0.59958
## APPROACH -0.35043 0.11693 -2.997 0.00290 **
## ASA2 0.26072 0.16560 1.574 0.11619
## PRI 0.02425 0.12382 0.196 0.84481
## TECHNIQUE -0.71195 0.17490 -4.071 5.66e-05 ***
## Laterality -0.01849 0.14408 -0.128 0.89797
## HERNIA_TYPE 0.02821 0.10775 0.262 0.79360
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.056 on 398 degrees of freedom
## Multiple R-squared: 0.1217, Adjusted R-squared: 0.1019
## F-statistic: 6.13 on 9 and 398 DF, p-value: 4.295e-08
summary(lm_robotLASSO)
##
## Call:
## lm(formula = Post_LOS ~ AGE_group + Sex + APPROACH + ASA2 + TECHNIQUE,
## data = ROBOT_NUMlm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.1335 -0.4014 -0.3058 0.0384 7.5352
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.08273 0.50377 6.119 2.23e-09 ***
## AGE_group 0.08594 0.07938 1.083 0.27962
## Sex 0.68066 0.21717 3.134 0.00185 **
## APPROACH -0.35383 0.11512 -3.074 0.00226 **
## ASA2 0.25827 0.16367 1.578 0.11536
## TECHNIQUE -0.71915 0.16978 -4.236 2.82e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.051 on 402 degrees of freedom
## Multiple R-squared: 0.1208, Adjusted R-squared: 0.1099
## F-statistic: 11.05 on 5 and 402 DF, p-value: 5.536e-10
summary(lm_robotAIC)
##
## Call:
## lm(formula = Post_LOS ~ Sex + APPROACH + ASA2 + TECHNIQUE, data = ROBOT_NUMlm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0564 -0.3986 -0.3570 -0.0564 7.5572
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.2671 0.4742 6.890 2.16e-11 ***
## Sex 0.6525 0.2157 3.026 0.00264 **
## APPROACH -0.3422 0.1146 -2.985 0.00301 **
## ASA2 0.3032 0.1584 1.915 0.05624 .
## TECHNIQUE -0.7410 0.1686 -4.395 1.42e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.051 on 403 degrees of freedom
## Multiple R-squared: 0.1182, Adjusted R-squared: 0.1095
## F-statistic: 13.51 on 4 and 403 DF, p-value: 2.417e-10
The model illustrates that patients with “senhance-robot” take 0.5 days less than “robot” patients. While it is just a estimation, there is a little bit difference between them.
summary(lm_robot1)
##
## Call:
## lm(formula = Post_LOS ~ APPROACH, data = ROBOT_NUMlm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.1318 -0.6022 -0.1318 0.3978 8.3978
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.0725 0.1625 18.90 < 2e-16 ***
## APPROACH -0.4704 0.1164 -4.04 6.41e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.094 on 406 degrees of freedom
## Multiple R-squared: 0.03864, Adjusted R-squared: 0.03627
## F-statistic: 16.32 on 1 and 406 DF, p-value: 6.406e-05